Enoch Yeung

Biography

Professor Enoch Yeung is an assistant professor in the Department of Mechanical Engineering at the University of California Santa Barbara. He has a B.S. in Mathematics from Brigham Young University, magnua cum laude with university honors and a Ph.D. in Control and Dynamical Systems from the California Institute of Technology. He has led many interdisciplinary research projects at the interface of synthetic biology and learning theory including the DARPA Synergistic Discovery & Design Program (currently serving as a performer and PI), DARPA Friend or Foe program (serving as co-PI), DARPA Living Foundries program, the 2018 High Performance Data Analytics Program, the NSF Molecular Programming Project, and the AFOSR Biological Research Initiative. Previously, he served as senior research scientist in the Data Science and Analytics Group at Pacific Northwest National Laboratory and lead several internal research efforts in deep learning, network inference, and control of complex systems. He has served on several advisory panels for DARPA, NIST, the DoD SBME initiative, and the National Defense University. He is the recipient of Kanel Foundation Fellowship, the National Science Foundation Graduate Fellowship, a National Defense Science and Engineering Fellowship, the PNNL Project Team of the Year Award, and the PNNL Outstanding Performance Award.

Research

Our lab studies how life enacts mechanisms of control, computation, and learning to engineer robust biological edge computing systems. Bacteria are extremely energy-efficient organisms that survive in the harshest of environments. Not only do they survive, but they solve complex fine-grained and binary classification problems regarding what resources to utilize, adapting to changes in environment and growth conditions at a moment's notice. How do we engineering living computer systems that exhibit the same functional properties? Life finds a way, and in so doing, seems to perform computation in ways that are fundamentally different from our laptops and CPUs. We seek to understand the fundamental laws by which bacteria make decisions and enact computational programs, especially in times of stress or environmental transition. We conduct our research in the Biological Control, Computing, and Learning Laboratory, which is an interdisciplinary group of researchers and students with backgrounds in control theory, synthetic biology, machine learning, dynamical systems, electrical, mechanical, and biological engineering. We address questions in biological computing and control with a mixture of experimental, computational, and theoretical methods.

Selected Publications

Hasnain, A., Boddupalli, N. and Yeung, E., 2019. Optimal reporter placement in sparsely measured genetic networks using the Koopman operator, to appear in the Proceedings of the 2019 IEEE Conference on Decision and Control.

Tschirhart, T., Shukla, V., Kelly, E.E., Schultzhaus, Z., NewRingeisen, E., Erickson, J.S., Wang, Z., Garcia, W., Curl, E., Egbert, R.G. and Yeung, E., 2019. Synthetic Biology Tools for the Fast-Growing Marine Bacterium Vibrio natriegens. ACS synthetic biology.

Hasnain, A., Sinha, S., Dorfan, Y., Borujeni, A. E., Park Y., Maschoff, P., Saxena, U., Urrutia, J., Gaffney, N., Becker, D., Siba, A., Maheshri, N, Gordon B., Voigt, C., and Yeung. E., 2019. Structured Dynamic Mode Decomposition for Quantifying a Circuit's Impact On Its Host, to appear in the Proceedings of the 2019 IEEE Biomedical Circuits and Systems Conference.

Khan, N., Yeung, E., Farris, Y., Fansler, S.J. and Bernstein, H.C., 2019. A broad-host-range event detector: expanding and quantifying performance across bacterial species, to appear in Synthetic Biology

Yeung, E., Kundu, S. and Hodas, N., 2019, July. Learning deep neural network representations for Koopman operators of nonlinear dynamical systems. In the Proceedings of the 2019 IEEE American Control Conference (ACC) (pp. 4832-4839).

Stinis, P., Hagge, T., Tartakovsky, A.M. and Yeung, E., 2019. Enforcing constraints for interpolation and extrapolation in generative adversarial networks. Journal of Computational Physics, 397, p.108844.

Yeung, E., Liu, Z. and Hodas, N.O., 2018, June. A koopman operator approach for computing and balancing gramians for discrete time nonlinear systems, in the Proceedings of the 2018 IEEE Annual American Control Conference (ACC) (pp. 337-344).

Yeung, E., Dy, A.J., Martin, K.B., Ng, A.H., Del Vecchio, D., Beck, J.L., Collins, J.J. and Murray, R.M., 2017. Biophysical constraints arising from compositional context in synthetic gene networks. Cell systems, 5(1), pp.11-24.

Niederholtmeyer, H., Sun, Z.Z., Hori, Y., Yeung, E., Verpoorte, A., Murray, R.M. and Maerkl, S.J., 2015. Rapid cell-free forward engineering of novel genetic ring oscillators. Elife, 4, p.e09771.

Sun, Z.Z., Yeung, E., Hayes, C.A., Noireaux, V. and Murray, R.M., 2013. Linear DNA for rapid prototyping of synthetic biological circuits in an Escherichia coli based TX-TL cell-free system. ACS synthetic biology, 3(6), pp.387-397.